Decision Theory and System Dynamics

Introduction

The world is facing major global challenges that move us toward or beyond social and ecological tipping points. In recent years this has increased the attention for systems thinking and system dynamics modeling to increase the level of innovation required to solve these global problems. There is a need to use integrative approaches to support transitions towards sustainability in general and the United Nations 2030 Agenda Sustainable Development Goals (SDGs) in specific. The practice of systems thinking, and modeling are fundamental for this, and an increasing understanding of complex dynamical behaviors is at the roots of applied sustainability science. Systems thinking and modeling are thus crucial for dealing with the complexity of our living world and its resources.

Many of the problems policymakers and managers face now arise as unanticipated side effects of their own past decisions. All too often the decisions made to solve important problems fail, make the problem worse, or create new problems. Effective decision making and learning in a world of growing dynamic complexity requires a different way of thinking: systems thinking. This means that decision-makers need to expand the boundaries of mental models and use tools to understand how the structure of complex systems creates their behavior.

This course introduces students to system dynamics as a tool for analyzing and modeling complex problems and strategies in both society (at large) and businesses. As such, system dynamics enables understanding the structure and dynamics of complex systems. System dynamics is also a rigorous modeling method for developing formal computer simulations of complex systems and use these simulations to design more effective policies and make better decisions. These simulation models can be used to create management flight simulators: microworlds where space and time can be compressed and slowed so decision makers can experience the long-term side effects of decisions, speed learning, develop our understanding of complex systems, and design structures and strategies for greater success (Sterman, 2000, pp. vii).

Course content

• Learning in and about complex systems
• The modeling process
• Causal loop diagramming
• Structure and behavior of dynamic systems
• Stocks and flows and their dynamics
• Dynamics of simple structures, like S-shaped growth
• Delays
• Modeling decision making and human behavior
• Supply chains and the origin of oscillations